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Introduction to Legal Technology, lecture 4 (2015)

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Slides for lecture 4 of the course Introduction to Legal Technology at the University of Turku Law School, presented Feb 3 2015.

This lecture combines different perspectives on the role of human factors in legal technology: legal reasoning as cognition and how to model it, and software usability as it relates to legal technology.

Published in: Law
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Introduction to Legal Technology, lecture 4 (2015)

  1. 1. TLS0070 Introduction to Legal Technology Lecture 4 Human factors University of Turku Law School 2015-02-03 Anna Ronkainen @ronkaine anna.ronkainen@onomatics.com
  2. 2. ‘Preliminary try-outs of decision machines built according to various formal specifications can be made in relation to selected administrative or judicial tribunals. The Supreme Court might be chosen for the purpose.’ (Harold Lasswell 1955)
  3. 3. ‘Can we “feed” into the computer that the judge’s ulcer is getting worse, that he had fought earlier in the morning with his wife, that the coffee was cold, that the defence counsel is an apparent moron, that the temporarily assigned associate judge is unfamiliar with the law and besides smokes obnoxious cigars, that the tailor’s bill was outrageous etc. etc.?’ (Kaarle Makkonen 1968, translation ar)
  4. 4. Say hi to System 1 (1/3) A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost? __ cents
  5. 5. Say hi to System 1 (2/3) If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets? __ minutes
  6. 6. Say hi to System 1 (3/3) In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake? __ days
  7. 7. If you got any of them wrong, you’re not alone... Table 1 CRT Scores, by Location Percentage scoring 0, 1, 2 or 3 Mean "Low” "High” Locations at which data were collected CRT score 0 1 2 3 N = Massachusetts Institute of Technology 2.18 7% 16% 30% 48% 61 Princeton University 1.63 18% 27% 28% 26% 121 Boston fireworks display* 1.53 24% 24% 26% 26% 195 Carnegie Mellon University 1.51 25% 25% 25% 25% 746 Harvard University* 1.43 20% 37% 24% 20% 51 University of Michigan: Ann Arbor 1.18 31% 33% 23% 14% 1267 Web-based studies* 1.10 39% 25% 22% 13% 525 Bowling Green University 0.87 50% 25% 13% 12% 52 University of Michigan: Dearborn 0.83 51% 22% 21% 6% 154 Michigan State University 0.79 49% 29% 16% 6% 118 University of Toledo 0.57 64% 21% 10% 5% 138 Overall 1.24 33% 28% 23% 17% 3428 (Frederick 2005)
  8. 8. ”As we know, there are known knowns. There are things we know we know. We also know there are known unknowns, that is to say, we know there are some things we do not know. But there are also unknown unknowns, the ones we don’t know we don’t know.” – Donald Rumsfeld (2002)
  9. 9. (Un)known (un)knowns known unknowns known knowns unknown unknowns ??
  10. 10. (Un)known (un)knowns known unknowns known knowns unknown unknowns unknown knowns
  11. 11. (Un)known (un)knowns conscious ignorance conscious knowledge unconscious ignorance unconscious knowledge
  12. 12. Dual-process cognition System 1 •  evolutionarily old •  unconscious, preconscious •  shared with animals •  implicit knowledge •  automatic •  fast •  parallel •  high capacity •  intuitive •  contextualized •  pragmatic •  associative •  independent of general intelligence System 2 •  evolutionarily recent •  conscious •  distinctively human •  explicit knowledge •  controlled •  slow •  sequential •  low capacity •  reflective •  abstract •  logical •  rule-based •  linked to general intelligence (Frankish & Evans 2009)
  13. 13. Systems 1 and 2 in legal reasoning: interaction System 1: making the decision System 2: validation and justification (Ronkainen 2011)
  14. 14. What’s that got to do with AI? -  MOSONG, my 1st (and so far only) system prototype -  built for studying the use of fuzzy logic in modelling various issues in legal theory -  specifically, the use of Type-2 fuzzy logic for modelling vagueness and uncertainty -  trademarks initially just a random example domain -  but the knowledge acquired through this research also proved useful for TrademarkNow...
  15. 15. Classical (crisp) logic 0 1 no yes
  16. 16. Fuzzy logic 0 0.5 1 no meh yes
  17. 17. Fuzzy logic 0 0.1 0.5 0.9 1 hell no no meh yes hell yes
  18. 18. Second-order/Type-2 fuzzy logic 0.1±0.1 0.5±0.2 0.9±0.1 no meh yes
  19. 19. Open texture ‘Whichever device, precedent or legislation, is chosen for the communication of standards of behaviour, these, however smoothly they work over the great mass of ordinary cases, will, at some point where their application is in question, prove indeterminate; they will have what has been termed an open texture.’ (Hart 1961)
  20. 20. Example of open texture : No vehicles in a park ‘When we are bold enough to frame some general rule of conduct (e.g. a rule that no vehicle may be taken into the park), the language used in this context fixes necessary conditions which anything must satisfy if it is to be within its scope, and certain clear examples of what is certainly within its scope may be present to our minds.’ (Hart 1961)
  21. 21. A park (and vehicles in it?) 1 2
  22. 22. A park...
  23. 23. And vehicles in it?
  24. 24. Inherent open texture: No boozing in a park Section 4 Intake of intoxicating substances The intake of intoxicating substances is prohibited in public places in built-up areas [...]. The provisions of paragraph 1 do not concern [...] the intake of alcoholic beverages in a park or in a comparable public place in a manner such that the intake or the presence associated with it does not obstruct unreasonably encumber other persons’ right to use the place for its intended purpose. (Public Order Act (612/2003))
  25. 25. Mosong: the domain Article 8 Relative grounds for refusal 1. Upon opposition by the proprietor of an earlier trade mark, the trade mark applied for shall not be registered: (a) if it is identical with the earlier trade mark and the goods or services for which registration is applied for are identical with the goods or services for which the earlier trade mark is protected; (b) if because of its identity with or similarity to the earlier trade mark and the identity or similarity of the goods or services covered by the trade marks there exists a likelihood of confusion on the part of the public in the territory in which the earlier trade mark is protected; the likelihood of confusion includes the likelihood of association with the earlier trade mark. [...] (CTM Regulation (40/94/EC))
  26. 26. Mosong: the domain Tentative rule Article 8 Relative grounds for refusal 1. Upon opposition by the proprietor of an earlier trade mark, the trade mark applied for shall not be registered: (a) if it is identical with the earlier trade mark and the goods or services for which registration is applied for are identical with the goods or services for which the earlier trade mark is protected; (b) if because of its identity with or similarity to the earlier trade mark and the identity or similarity of the goods or services covered by the trade marks there exists a likelihood of confusion on the part of the public in the territory in which the earlier trade mark is protected; the likelihood of confusion includes the likelihood of association with the earlier trade mark. REFUSAL = MARKS-SIMILAR and GOODS-SIMILAR
  27. 27. ‘Training’ set: 119 cases (Salmi et al 2001)
  28. 28. Training set 119 cases from 1997–2000, of which 107 from the Opposition Division (1st instance) and 12 from the Boards of Appeal (2nd instance)
  29. 29. Results for the training set 0 0.2 0.4 0.6 0.8 1
  30. 30. Validation set 30 most recent (2002) relevant cases: 20 from the Opposition Division and 10 from the Boards of Appeal Result*: all cases predicted correctly * when coded into the system by a domain expert
  31. 31. Results for the validation set 0 0.2 0.4 0.6 0.8 1 Opposition Division Boards of Appeal
  32. 32. Non-expert validation •  done by non-law students taking a course on intellectual property law (n=75) •  original validation set in two parts (15+15 cases) at the beginning and the end of the course •  completed non-interactively through a web form •  correct answer: 54.6±6.5% •  incorrect answer: 25.9±7.5% •  no answer: 19.5±5.2% (± = σ)
  33. 33. Non-expert validation % ±stderr before after own total group 1 (n=15) 41.3±1.7 65.8±2.8 53.5±1.7 group 2 (n=12) 46.1±2.0 65.0±3.0 55.6±1.9 group 3 (n=48) 43.3±1.3 65.9±1.3 54.7±0.9 total (n=75) 43.4±1.0 65.8±1.1 54.6±0.8
  34. 34. Initial conclusions from this work -  it (sort of) works; using fuzzy logic makes sense in this context -  poses more questions than it answers... -  ...and that’s how I ended up tryin to reverse- engineer human lawyers rather than just trying to build systems based on existing legal theory literature
  35. 35. Implications for legal AI -  using rule-based methods has its advantages -  human-readable -  comparatively quick to develop -  modifiable (esp. relevant wrt legislative changes) -  but they can’t do the work alone -  can’t make sense about situations which they weren’t specifically built to handle -  real-world complexity needs (sometimes) statistical/machine-learning approaches
  36. 36. Design thinking
  37. 37. Law and design ?
  38. 38. Design in law -  not (just) about the esthetics of physical object (wrong faculty for that) -  not about the legal protection of designs (wrong course for that) -  design as a way to rethink business processes in law... -  ...and as a way to think about the use of information in legal applications (UI/UX design)
  39. 39. Design thinking -  Peter Drucker: the job of designers is “converting need into demand” – figuring out what people want and giving it to them (i.e., innovating) -  Tim Brown of IDEO: The challenge for design thinkers is to “help people to articulate the latent needs they may not even know they have” -  desirable, viable, feasible
  40. 40. Nudging -  design thinking in (eg) governmental services -  manipulating the choice architecture to help people make better choices (unconsciously) -  example: organ donation opt-in vs. opt-out, consent rate ~10% vs. >99%
  41. 41. Business model innovation through service design: Wevorce
  42. 42. Wevorce -  “turning every divorce amicable” -  started in 2012, Y Combinator W13 alumn, founded in Boise, ID, but moved to Silicon Valley, “divorce architects” operating in ~30 markets across the US -  $2M in venture capital funding
  43. 43. Usability in legal informatics
  44. 44. A tale of two electric kettles
  45. 45. A tale of two electric kettles
  46. 46. What is usability? “Usability is the extent to which a system can be used by specific users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use.” ISO 9241-210
  47. 47. What is usability? “It is important to realize that usability is not a single, one-dimensional property of the user interface. Usability has multiple components and is traditionally associated with these five usability attributes: -  Learnability: The system should be easy to learn so that the user can rapidly start getting some work done with the system. -  Efficiency: The system should be efficient to use, so that once the user has learned the system, a high level of productivity is possible. -  Memorability: The system should be easy to remember, so that the casual user is able to return to the system after some period of not having used it, without having to learn everything all over again. -  Errors: The system should have a low error rate, so that users make few errors during the use of the system, and so that if they do make errors they can easily recover from them. Further, catastrophic errors must not occur. -  Satisfaction: The system should be pleasant to use, so that users are subjectively satisfied when using it; they like it.” Nielsen 1993
  48. 48. Levels of usability mental model high-level represented model low-level represented model implementation model
  49. 49. Levels of usability: law mental model high-level represented model low-level represented model implementation model §§
  50. 50. How to implement usability -  evaluation of current systems and processes -  field studies -  mock-ups, paper prototypes -  iterative development -  heuristic evaluation by an expert -  end-user usability testing
  51. 51. How to implement usability -  evaluation of current systems and processes -  field studies -  mock-ups, paper prototypes -  iterative development -  heuristic evaluation by an expert -  end-user usability testing
  52. 52. Software is not always the answer! Our project management solution: (... until a month ago)
  53. 53. Legal regulation of usability in Finland... For example: -  EN 62366:2008 Medical devices - Application of usability engineering to medical devices authorized by Chapter 2 of the Medical Supplies and Equipment Act (629/2010) -  CLC/TS 50459:2005 Railway applications. Communication, signalling and processing systems. European rail traffic management system. Driver-machine interface. Data entry for the ERTMS/ETCS/GSM-R systems, authorized by 28 § 2 of the Railways Act (555/2006)
  54. 54. ...and why it might be a good idea...
  55. 55. ...seriously (Viitanen et al 2011)
  56. 56. Levels of usability mental model high-level represented model low-level represented model implementation model
  57. 57. most important results at the top
  58. 58. line break technology hyperlinks to docs
  59. 59. bullet point technology
  60. 60. codes explained in context
  61. 61. Good usability is good for -  increased productivity -  reducing training and support costs -  speeding up development -  speeding up legal processes -  quality of legal decisions -  occupational well-being So why isn’t there more of it in the legal field? And why (almost) no research?
  62. 62. Questions?

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